The distinction is that it's not 'predicting the next token'. Instead it's _determining_ the next token based on a prediction of its reward signal.
Also known as predicting.
An LLM extrapolates from its context window to the immediate next token. This word applies whether you view what's happening as "reasoning", "prediction", or as a math function.
If I steer a car to avoid a predicted collision with a wall, this is not me 'predicting' the car. I am steering the car based on a prediction.
And if you didn't make the final decision on the action yourself that would be worth mentioning.
Like stanleykm, I found this analogy somewhat puzzling. On reflection, I think the author's point is this: the statistics of actual usage do not seem sufficient to produce a fluent LLM; it also takes reinforcement learning.